Model-based delineation of organs is an efficient and robust way to segment medical images. In this approach, a model of the organ is adapted to the image, thereby delineating the organ. Such methods were described, for example, in Jürgen Weese, Michael Kaus, Christian Lorenz, Steven Lobregt, Roel Truyen and Vladimir Pekar's Shape Constrained Deformable Models for 3D Medical Image Segmentation, Lecture Notes in Computer Science, 2001, Volume 2082/2001, pages 380-387, hereinafter referred to as Ref. 1, and many other papers co-authored by any of the authors of Ref. 1. However, known models are typically fairly rigid and thus their deformation during adaptation to the image is small. Therefore, this approach often fails when applied to segment images depicting organs with high shape variability, especially when the organ shape variants are topologically not equivalent. Examples of such organs include the left atrium of the heart having many variants comprising different numbers of pulmonary veins draining into it, or the kidneys having many different arterial feeding connections.